Marginal hazard modeling in the presence of left censoring and unobserved history
Bibliographic record
Abstract
Effective antiretroviral therapy (ART) has led to a great reduction in the rates of mortalityand morbidity in patients infected with the human immunodeficiency virus (HIV). However, despite the established benefits of ART in reducing almost all illnesses among HIV infected individuals, the impact of ART on liver disease in HIV and hepatitis C virus (HCV) co-infected individuals is not well understood. While ART remains the only approach to HIV treatment, its benefits in HIV-HCV co-infected individuals may be offset by its hepatotoxicity in a population in which rates of irreversible liver injury, alcohol use, and illicit drug use may be high. In addition to these factors, some studies hypothesize that liver disease in ART-treated HIV-HCV co-infected individuals may be associated with ART interruptions.Determining the impact of ART interruptions on liver disease such as fibrosis is of great importance since liver fibrosis affects many people and is a significant cause of morbidity and mortality among co-infected individuals. In this thesis, data from the Canadian Co-Infection Cohort (CCC) are used to determine the impact of ART interruptions on liver fibrosis in HIV-HCV co-infected adults. The CCC is unique in the sense that ART interruption | the time dependent exposure | may precede the time of enrolment. The analysis of CCC data is methodologically challenging for the following reasons: (i) a significant proportion of participants have partially unobserved histories of both confounders and time dependent exposure; (ii) some participants had experienced the event by the time of enrolment, and thus have completely unobserved histories and furtherhave left censored event times; and (iii) the possibility of bias due to non participation because co-infected individuals decide entirely for themselves whether or not to enrol. I address both partially and completely unobserved histories using an imputation based approach that "recovers" the unobserved time dependent data, simultaneously eliminating the problem of left censoring. These approaches allow for the use of the complete history and the inclusion of all participants in a Cox marginal structural model. The proposed approach to address the non participation bias consists of incorporating the inverse probability of participation weights into the models used to estimate the causal hazard ratio of ART interruptionson liver fibrosis. Although the proposed approaches are motivated by and applied to the CCC,they can be used in many studies of chronic disease in which the exposure precedes studyentry and the outcome event does not preclude further follow up.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".